Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method of defect review, comprising: receiving a defect map from a defect scanner, wherein the defect map comprises at least one defect location of a semiconductor workpiece; annotating the defect map with a reference fiducial location of the semiconductor workpiece; determining a detected fiducial location within image data of the semiconductor workpiece, wherein the detected fiducial location is based on an alphanumeric pattern provided in a scribe line of the semiconductor workpiece; determining an offset correction based on comparing the detected fiducial location with the reference fiducial location; producing a corrected defect map by applying the offset correction to the defect map, wherein the applying the offset correction translocates the at least one defect location; and transferring the corrected defect map to a defect reviewer configured to perform root cause analysis based on the corrected defect map.
2. The method of claim 1 , wherein the detected fiducial location is based on detection of a fiducial pattern within the image data.
This invention relates to image processing techniques for detecting fiducial patterns within image data, particularly in applications such as augmented reality, robotics, or computer vision. The problem addressed is accurately identifying and locating fiducial markers in images, which are often used as reference points for spatial positioning, object tracking, or environmental mapping. The method involves analyzing image data to detect a fiducial pattern, which is a predefined visual marker designed to be easily recognizable by image processing algorithms. The detected fiducial pattern is then used to determine its precise location within the image. This location data can be used for various purposes, such as aligning virtual objects with real-world coordinates in augmented reality systems, guiding robotic movements, or calibrating camera systems. The detection process may involve pattern recognition techniques, such as template matching, feature extraction, or machine learning-based classification, to identify the fiducial pattern within the image. Once detected, the method calculates the fiducial's position, which may include its coordinates, orientation, or other spatial attributes. The method ensures robustness against variations in lighting, perspective, or image noise, making it suitable for real-world applications where environmental conditions may vary. By accurately detecting and locating fiducial patterns, this technique enables precise spatial referencing, improving the reliability of systems that depend on such markers for navigation, tracking, or interaction with physical environments.
3. The method of claim 2 , wherein the fiducial pattern is overlaid with a scale or a cursor.
A method for enhancing the visibility and usability of fiducial patterns in imaging systems addresses the challenge of accurately identifying and aligning fiducial markers in digital images. Fiducial patterns, which are reference markers used for calibration, alignment, or tracking in imaging applications, often require precise detection and measurement. The method improves upon existing techniques by overlaying the fiducial pattern with a scale or cursor, providing a visual aid to assist users in measuring or aligning the pattern. The scale allows for precise distance measurements between fiducial elements, while the cursor enables interactive selection or adjustment of the pattern. This overlay can be dynamically adjusted based on the imaging context, such as magnification level or coordinate system requirements. The method ensures that the fiducial pattern remains clearly visible and measurable, even in complex or cluttered environments, improving accuracy in applications like medical imaging, robotics, or augmented reality. The overlay can be generated in real-time or pre-processed, depending on the system's requirements, and may include additional features like grid lines or coordinate indicators to further enhance usability.
4. The method of claim 1 , further comprising determining the detected fiducial location within image data of the semiconductor workpiece centered at the reference fiducial location.
The invention relates to semiconductor manufacturing, specifically to methods for aligning semiconductor workpieces using fiducial markers. The problem addressed is the precise detection and alignment of fiducial markers on semiconductor workpieces to ensure accurate positioning during manufacturing processes. Traditional methods may struggle with variations in fiducial marker visibility or positioning errors, leading to misalignment and defects. The method involves detecting a fiducial marker on a semiconductor workpiece and determining its location relative to a reference fiducial location. This includes capturing image data of the workpiece and analyzing the image to identify the fiducial marker. The detected fiducial location is then compared to the reference fiducial location to assess alignment accuracy. Additionally, the method determines the detected fiducial location within the image data centered at the reference fiducial location, ensuring precise spatial correlation between the actual and expected positions. This step helps correct for any deviations, improving alignment accuracy in subsequent manufacturing steps. The method may also involve adjusting the workpiece position based on the detected fiducial location to achieve optimal alignment. This approach enhances manufacturing precision, reducing defects and improving yield.
5. The method of claim 1 , wherein the determining the detected fiducial location comprises determining whether a possible fiducial pattern found in the image data overlaps with a fiducial contour.
This invention relates to image processing techniques for detecting and verifying fiducial markers in image data, particularly in applications such as augmented reality, robotics, or computer vision. The problem addressed is the accurate and reliable identification of fiducial markers, which are predefined patterns used as reference points in images. Existing methods may struggle with false positives or misalignment due to noise, occlusion, or partial visibility of the fiducial markers. The method involves analyzing image data to locate potential fiducial patterns. Once a possible fiducial pattern is identified, the system checks whether it overlaps with a fiducial contour—a predefined boundary or shape that the fiducial marker should conform to. This step ensures that the detected pattern is not only a candidate fiducial but also properly aligned with the expected contour, reducing false positives. The contour may be derived from prior knowledge of the fiducial's shape or from additional image analysis. By verifying the overlap, the method improves the accuracy of fiducial detection, making it more robust in real-world applications where markers may be partially obscured or distorted. This technique is particularly useful in dynamic environments where precise tracking of fiducial markers is critical.
6. The method of claim 1 , wherein the determining the detected fiducial location comprises determining whether a percentage of overlap between a possible fiducial pattern and a fiducial contour exceeds an edge threshold.
This invention relates to fiducial detection in imaging systems, addressing the challenge of accurately identifying fiducial markers in images despite noise, distortion, or partial occlusion. Fiducial markers are predefined patterns used for alignment, calibration, or tracking in applications like robotics, augmented reality, and medical imaging. The method determines the location of a detected fiducial by evaluating the overlap between a possible fiducial pattern and a fiducial contour. Specifically, it calculates whether the percentage of overlap exceeds a predefined edge threshold. If the overlap meets or exceeds this threshold, the system confirms the fiducial location. This approach improves detection reliability by reducing false positives caused by partial matches or similar patterns. The method may also involve preprocessing the image to enhance contrast or reduce noise before contour extraction. The fiducial contour is derived from edge detection or segmentation techniques, and the possible fiducial pattern is compared against this contour to assess alignment. The edge threshold is a configurable parameter that balances sensitivity and accuracy, ensuring robust detection under varying conditions. This technique is particularly useful in dynamic environments where fiducials may be partially obscured or distorted.
7. The method of claim 6 , wherein the fiducial contour comprises at least one contour feature.
A system and method for enhancing the accuracy of fiducial detection in imaging applications, particularly in medical imaging or automated manufacturing, addresses the challenge of precisely identifying and tracking reference points (fiducials) in complex or noisy environments. The method involves generating a fiducial contour, which is a boundary or outline representing the fiducial's shape, to improve detection reliability. The contour includes at least one distinct contour feature, such as a corner, edge, or other geometric characteristic, that aids in distinguishing the fiducial from surrounding structures or artifacts. This feature-based approach enhances the robustness of fiducial detection algorithms by providing additional discriminative information beyond simple point-based or area-based detection methods. The contour may be generated using image processing techniques, such as edge detection or segmentation, and can be dynamically adjusted based on environmental conditions or imaging parameters. The inclusion of contour features allows for more precise alignment and tracking, reducing errors in applications like surgical navigation, robotic assembly, or quality control. The method ensures that fiducials remain detectable even in low-contrast or cluttered scenes, improving overall system accuracy and reliability.
8. The method of claim 1 , further comprising determining multiple detected fiducial locations within the image data, wherein the offset correction is based on comparing each of the multiple detected fiducial locations with respective reference fiducial locations.
This invention relates to image processing, specifically to correcting positional offsets in image data by using fiducial markers. The problem addressed is the misalignment of detected fiducial locations in an image compared to their expected positions, which can occur due to distortions, sensor inaccuracies, or environmental factors. The solution involves detecting multiple fiducial markers within the image data and comparing their positions to predefined reference fiducial locations. By analyzing these comparisons, an offset correction is calculated to adjust the image data, ensuring accurate alignment. The method improves the precision of image-based measurements, inspections, or tracking systems where positional accuracy is critical. The fiducial markers may be predefined patterns or features embedded in the image or scene, and the correction accounts for variations in their detected positions relative to the reference locations. This approach enhances the reliability of systems relying on fiducial-based alignment, such as robotics, augmented reality, or quality control in manufacturing. The correction may involve translation, rotation, or scaling adjustments to minimize discrepancies between detected and reference fiducial positions.
9. A method for defect map correction, comprising: receiving a defect map from a defect scanner, wherein the defect map comprises a plurality of defect locations; annotating the defect map with a plurality of reference fiducial locations; determining multiple detected fiducial locations within image data of a semiconductor workpiece, wherein each of the multiple detected fiducial locations is based on respective multiple alphanumeric patterns provided in one or more scribe lines of the semiconductor workpiece; determining a plurality of offset corrections based on comparing each of the multiple detected fiducial locations with respective reference fiducial locations; and producing a corrected defect map by applying the plurality of offset corrections to the defect map, wherein the applying the plurality of offset corrections translocates each of the plurality of defect locations.
The invention relates to defect map correction in semiconductor manufacturing, addressing inaccuracies in defect detection due to misalignment between defect scanner data and the physical workpiece. Defect scanners generate maps indicating defect locations, but these maps may contain positional errors caused by misalignment between the scanner's coordinate system and the workpiece's actual fiducial markers. The method corrects these errors by aligning the defect map with the workpiece's physical reference points. The process begins by receiving a defect map from a defect scanner, which includes multiple defect locations. The map is then annotated with reference fiducial locations, which serve as known reference points on the workpiece. Next, the method detects multiple fiducial locations within image data of the semiconductor workpiece. Each detected fiducial location is identified based on alphanumeric patterns present in one or more scribe lines of the workpiece. By comparing these detected fiducial locations with the reference fiducial locations, the method calculates offset corrections. These corrections account for any positional discrepancies between the scanner's detected fiducials and the actual reference fiducials. Finally, the method applies these offset corrections to the defect map, adjusting the positions of all defect locations to produce a corrected defect map. This ensures accurate defect localization for subsequent inspection and repair processes.
10. The method of claim 9 , wherein the plurality of reference fiducial locations comprises six reference fiducial locations.
This invention relates to a method for determining the position and orientation of a device using reference fiducial locations. The method addresses the challenge of accurately tracking the spatial position and orientation of a device in a three-dimensional space, which is critical in applications such as robotics, augmented reality, and medical imaging. The method involves using a plurality of reference fiducial locations to establish a coordinate system and then determining the device's position and orientation relative to these fiducials. The method includes capturing images or sensor data of the fiducial locations, processing the data to identify the fiducials, and calculating the device's pose based on the known positions of the fiducials. The invention specifies that the plurality of reference fiducial locations consists of six distinct fiducial locations, which provides sufficient geometric constraints to uniquely determine the device's position and orientation in three-dimensional space. The use of six fiducials ensures redundancy and robustness, reducing errors caused by occlusions or measurement noise. The method may be applied in various fields where precise spatial tracking is required, such as industrial automation, navigation systems, and virtual reality environments.
11. The method of claim 9 , wherein the applying the plurality of offset corrections to the defect map is performed for each of the plurality of offset corrections serially.
A method for defect inspection in semiconductor manufacturing involves generating a defect map from inspection data and applying multiple offset corrections to this map to improve accuracy. The defect map is created by analyzing inspection data from a semiconductor wafer, identifying defects, and mapping their locations. The method then applies a series of offset corrections to the defect map, where each correction adjusts the positions of the detected defects based on known or estimated misalignments in the inspection system or wafer positioning. These corrections are applied one at a time, in sequence, to progressively refine the defect locations. The process ensures that each correction is applied independently, allowing for precise adjustment of defect positions without interference from other corrections. This approach enhances the accuracy of defect mapping, which is critical for identifying and addressing manufacturing defects in semiconductor production. The method is particularly useful in advanced semiconductor processes where precise defect localization is essential for yield improvement.
12. The method of claim 9 , wherein each of the multiple detected fiducial locations are determined based on detection of a fiducial pattern within the image data.
The invention relates to a method for detecting fiducial locations within image data, particularly in applications such as augmented reality, robotics, or computer vision. The problem addressed is accurately identifying predefined fiducial patterns in images to determine their spatial positions, which is essential for tasks like object tracking, pose estimation, or environmental mapping. The method involves analyzing image data to detect multiple fiducial locations. Each fiducial location is determined by identifying a fiducial pattern within the image data. The fiducial pattern may be a specific marker, symbol, or geometric configuration designed to be easily recognizable by image processing algorithms. Once detected, the method extracts the spatial coordinates of these fiducial locations, enabling further processing such as alignment, navigation, or interaction with the physical environment. The method may also include preprocessing the image data to enhance fiducial detection, such as applying filters to reduce noise or improve contrast. Additionally, the method may involve comparing detected fiducial patterns against a reference database to confirm their identity and ensure accuracy. The detected fiducial locations can then be used for various applications, including augmented reality overlays, robotic navigation, or automated quality inspection in manufacturing. The invention improves upon existing techniques by providing a robust and efficient way to detect fiducial patterns, ensuring reliable spatial positioning in dynamic or complex environments.
13. The method of claim 9 , further comprising: transferring the corrected defect map to a defect reviewer configured to perform root cause analysis based on the corrected defect map, wherein the root cause analysis analyzes at least one defect at each of the plurality of defect locations to determine at least one common cause of the at least one defect at each of the plurality of defect locations.
This invention relates to semiconductor manufacturing defect analysis, specifically improving root cause analysis by correcting defect map data before review. During semiconductor production, defects are detected and mapped, but these maps often contain errors due to noise, misalignment, or false positives. The invention addresses this by processing the defect map to correct these errors, ensuring accurate defect locations and characteristics. The corrected map is then transferred to a defect reviewer system, which performs root cause analysis by examining defects at multiple locations to identify common causes. The analysis may involve statistical or pattern-based methods to determine shared origins of defects, such as process variations or equipment issues. By improving defect map accuracy, the system enhances the reliability of root cause analysis, enabling more effective defect mitigation and yield improvement in semiconductor manufacturing. The method integrates defect correction with downstream analysis, streamlining the troubleshooting process.
14. A defect offset module, comprising: a network connection module configured to: access a defect map, wherein the defect map comprises at least one defect location; at least one processor configured to: annotate the defect map with a reference fiducial location; determine a detected fiducial location within image data of a semiconductor workpiece, wherein the detected fiducial location is based on an alphanumeric pattern provided in a scribe line of the semiconductor workpiece; determine an offset correction based on comparing the detected fiducial location with an associated reference fiducial location; and produce a corrected defect map by applying the offset correction to the defect map, wherein the applying the offset correction translocates the at least one defect location, wherein the network connection module is further configured to provide the corrected defect map to a defect reviewer configured to perform root cause analysis based on the corrected defect map.
This invention relates to semiconductor manufacturing, specifically addressing misalignment issues in defect mapping. During semiconductor production, defect maps are generated to identify and analyze defects on a workpiece, but these maps may be misaligned due to inaccuracies in fiducial detection. The invention provides a defect offset module that corrects this misalignment by comparing a detected fiducial location in the workpiece's scribe line (identified via an alphanumeric pattern) with a reference fiducial location from the defect map. The module calculates an offset correction and applies it to the defect map, adjusting the positions of all defect locations accordingly. The corrected map is then provided to a defect reviewer for root cause analysis. The system ensures accurate defect localization, improving yield analysis and troubleshooting in semiconductor fabrication. The module includes a network connection for accessing the defect map and transmitting the corrected data, along with a processor to perform the alignment calculations. This solution enhances the reliability of defect inspection and review processes.
15. The defect offset module of claim 14 , wherein the network connection module is at least one of: a network bus and network transceiver.
This invention relates to electronic circuit testing and the identification of defects. Specifically, it addresses the problem of accurately locating and characterizing defects within integrated circuits. The system includes a defect offset module designed to determine the spatial offset of a detected defect from a reference point. This module is connected to a network connection module. The network connection module facilitates communication and data transfer within the system. It is implemented as at least one of a network bus, which provides a shared communication pathway, or a network transceiver, which handles the transmission and reception of data over a network. The defect offset module, in conjunction with the network connection module, enables the precise localization of circuit defects, which is crucial for efficient debugging and manufacturing process improvement.
16. The defect offset module of claim 14 , wherein the detected fiducial location is based on detection of a fiducial pattern within the image data.
A system and method for defect detection in manufacturing processes, particularly in semiconductor or electronic component production, addresses the challenge of accurately identifying and correcting defects in high-precision manufacturing. The system includes a defect offset module that processes image data of a manufactured component to detect and analyze defects. The module determines the location of a fiducial pattern within the image data, which serves as a reference point for defect detection. By aligning the detected fiducial location with a known reference position, the system compensates for misalignment or positional errors in the manufacturing process. This allows for precise defect identification and correction, improving yield and quality control. The fiducial pattern may be a predefined marker or feature embedded in the component or substrate, detectable through image processing techniques such as pattern recognition or edge detection. The system may further include calibration mechanisms to adjust for variations in imaging conditions or component positioning. The defect offset module integrates with other modules to provide real-time feedback for process adjustments, ensuring consistent defect detection and correction across multiple production cycles.
17. The defect offset module of claim 14 , wherein the defect offset module is contained within a single housing with at least one of: a defect scanner and the defect reviewer.
This invention relates to a defect inspection system for semiconductor manufacturing, addressing the challenge of efficiently detecting and reviewing defects in semiconductor wafers. The system includes a defect offset module integrated within a single housing that also contains either a defect scanner or a defect reviewer, or both. The defect offset module is designed to adjust the position of a wafer relative to the scanner or reviewer to ensure accurate defect detection and analysis. The system automates the alignment process, reducing manual intervention and improving throughput. The defect scanner identifies potential defects on the wafer surface, while the defect reviewer provides high-resolution imaging to confirm and analyze the defects. By housing the defect offset module with the scanner or reviewer, the system minimizes mechanical movement and alignment errors, enhancing precision and reliability. The integrated design also reduces the footprint of the inspection system, making it more compact and suitable for high-volume manufacturing environments. The invention aims to improve defect detection accuracy and streamline the inspection workflow in semiconductor fabrication.
18. The defect offset module of claim 14 , wherein the at least one processor is further configured to compare a possible fiducial pattern, found in the image data, with a fiducial contour.
The invention relates to defect detection in manufacturing processes, particularly for identifying and correcting misalignments or defects in fiducial patterns used for alignment or quality control. The system captures image data of a workpiece or substrate and analyzes it to detect fiducial patterns, which are reference marks used for precise alignment in manufacturing. The challenge addressed is ensuring accurate detection and alignment despite potential defects or distortions in the fiducial patterns, which can lead to misalignment errors in subsequent manufacturing steps. The defect offset module processes the image data to identify a possible fiducial pattern and compares it against a predefined fiducial contour. The fiducial contour represents the expected shape or boundary of a properly formed fiducial pattern. By comparing the detected pattern with the contour, the system can determine if there is a defect or misalignment. This comparison helps in assessing whether the fiducial pattern is usable for alignment purposes or if adjustments are needed to compensate for the defect. The system may then apply corrective measures, such as adjusting alignment parameters or flagging the workpiece for further inspection. This ensures that manufacturing processes maintain precision despite variations in fiducial pattern quality.
19. The defect offset module of claim 14 , wherein the network module is further configured to annotate the defect map with a plurality of reference fiducial locations.
A system for defect detection and mapping in manufacturing processes, particularly for identifying and analyzing defects in semiconductor wafers or similar substrates, addresses the challenge of accurately locating and tracking defects during production. The system includes a defect offset module that processes defect data to generate a defect map, which visually represents the spatial distribution of defects on a substrate. The defect offset module incorporates a network module designed to enhance the defect map by annotating it with reference fiducial locations. These fiducial locations serve as precise reference points on the substrate, enabling accurate alignment and correlation of defect positions with known features or markers. The network module dynamically integrates these fiducial annotations into the defect map, improving defect localization and facilitating defect analysis by providing a standardized reference framework. This annotation process ensures that defects can be consistently tracked across multiple inspection stages or substrates, enhancing manufacturing quality control and process optimization. The system may also include additional modules for defect classification, measurement, and reporting, all contributing to a comprehensive defect management solution.
20. The defect offset module of claim 14 , wherein the network module is further configured to transfer the corrected defect map to a defect reviewer configured to perform root cause analysis based on the corrected defect map, wherein the root cause analysis analyzes at least one defect at each of the plurality of defect locations to determine at least one common cause of the at least one defect at each of the plurality of defect locations.
This invention relates to defect analysis in semiconductor manufacturing, specifically improving defect detection and root cause analysis. The system includes a defect offset module that processes defect data from a semiconductor wafer to generate a corrected defect map. This module corrects positional inaccuracies in defect detection by aligning defect locations with reference points on the wafer, such as alignment marks or die boundaries. The corrected defect map is then transferred to a defect reviewer, which performs root cause analysis by examining defects at multiple locations to identify common causes. The analysis may involve statistical or pattern-based methods to determine shared factors contributing to defects across the wafer. The system enhances defect review efficiency by providing accurate defect positioning and enabling systematic root cause identification, reducing time and resources spent on manual inspection. The technology addresses challenges in semiconductor manufacturing where defect misalignment and unclear root causes lead to inefficiencies in yield improvement.
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November 17, 2020
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